Overview

Dataset statistics

Number of variables11
Number of observations16598
Missing cells329
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory88.0 B

Variable types

Numeric7
Text2
Categorical2

Alerts

EU_Sales is highly overall correlated with Global_Sales and 3 other fieldsHigh correlation
Global_Sales is highly overall correlated with EU_Sales and 3 other fieldsHigh correlation
NA_Sales is highly overall correlated with EU_Sales and 3 other fieldsHigh correlation
Other_Sales is highly overall correlated with EU_Sales and 3 other fieldsHigh correlation
Platform is highly overall correlated with YearHigh correlation
Rank is highly overall correlated with EU_Sales and 3 other fieldsHigh correlation
Year is highly overall correlated with PlatformHigh correlation
Year has 271 (1.6%) missing valuesMissing
Other_Sales is highly skewed (γ1 = 24.23392253)Skewed
Rank is uniformly distributedUniform
Rank has unique valuesUnique
NA_Sales has 4499 (27.1%) zerosZeros
EU_Sales has 5730 (34.5%) zerosZeros
JP_Sales has 10455 (63.0%) zerosZeros
Other_Sales has 6477 (39.0%) zerosZeros

Reproduction

Analysis started2026-01-19 14:32:26.769943
Analysis finished2026-01-19 14:32:37.967555
Duration11.2 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Rank
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct16598
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8300.6053
Minimum1
Maximum16600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:38.382497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile831.85
Q14151.25
median8300.5
Q312449.75
95-th percentile15770.15
Maximum16600
Range16599
Interquartile range (IQR)8298.5

Descriptive statistics

Standard deviation4791.8539
Coefficient of variation (CV)0.5772897
Kurtosis-1.1998649
Mean8300.6053
Median Absolute Deviation (MAD)4149.5
Skewness6.6497168 × 10-5
Sum1.3777345 × 108
Variance22961864
MonotonicityStrictly increasing
2026-01-19T20:32:38.762077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166001
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
165841
 
< 0.1%
165831
 
< 0.1%
165821
 
< 0.1%
165811
 
< 0.1%
Other values (16588)16588
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
166001
< 0.1%
165991
< 0.1%
165981
< 0.1%
165971
< 0.1%
165961
< 0.1%
165951
< 0.1%
165941
< 0.1%
165931
< 0.1%
165921
< 0.1%
165911
< 0.1%

Name
Text

Distinct11493
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:39.673115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length132
Median length92
Mean length23.966622
Min length1

Characters and Unicode

Total characters397798
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8718 ?
Unique (%)52.5%

Sample

1st rowWii Sports
2nd rowSuper Mario Bros.
3rd rowMario Kart Wii
4th rowWii Sports Resort
5th rowPokemon Red/Pokemon Blue
ValueCountFrequency (%)
the2733
 
4.2%
of1718
 
2.6%
21172
 
1.8%
759
 
1.2%
no748
 
1.1%
3545
 
0.8%
world401
 
0.6%
pro320
 
0.5%
game308
 
0.5%
ii295
 
0.4%
Other values (9274)56814
86.3%
2026-01-19T20:32:40.902015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49220
 
12.4%
e32121
 
8.1%
a26543
 
6.7%
o24139
 
6.1%
r21134
 
5.3%
i20825
 
5.2%
n20463
 
5.1%
t17111
 
4.3%
s15473
 
3.9%
l12353
 
3.1%
Other values (87)158416
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)397798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
49220
 
12.4%
e32121
 
8.1%
a26543
 
6.7%
o24139
 
6.1%
r21134
 
5.3%
i20825
 
5.2%
n20463
 
5.1%
t17111
 
4.3%
s15473
 
3.9%
l12353
 
3.1%
Other values (87)158416
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)397798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
49220
 
12.4%
e32121
 
8.1%
a26543
 
6.7%
o24139
 
6.1%
r21134
 
5.3%
i20825
 
5.2%
n20463
 
5.1%
t17111
 
4.3%
s15473
 
3.9%
l12353
 
3.1%
Other values (87)158416
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)397798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
49220
 
12.4%
e32121
 
8.1%
a26543
 
6.7%
o24139
 
6.1%
r21134
 
5.3%
i20825
 
5.2%
n20463
 
5.1%
t17111
 
4.3%
s15473
 
3.9%
l12353
 
3.1%
Other values (87)158416
39.8%

Platform
Categorical

High correlation 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
DS
2163 
PS2
2161 
PS3
1329 
Wii
1325 
X360
1265 
Other values (26)
8355 

Length

Max length4
Median length3
Mean length2.7667189
Min length2

Characters and Unicode

Total characters45922
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWii
2nd rowNES
3rd rowWii
4th rowWii
5th rowGB

Common Values

ValueCountFrequency (%)
DS2163
13.0%
PS22161
13.0%
PS31329
 
8.0%
Wii1325
 
8.0%
X3601265
 
7.6%
PSP1213
 
7.3%
PS1196
 
7.2%
PC960
 
5.8%
XB824
 
5.0%
GBA822
 
5.0%
Other values (21)3340
20.1%

Length

2026-01-19T20:32:41.142760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ds2163
13.0%
ps22161
13.0%
ps31329
 
8.0%
wii1325
 
8.0%
x3601265
 
7.6%
psp1213
 
7.3%
ps1196
 
7.2%
pc960
 
5.8%
xb824
 
5.0%
gba822
 
5.0%
Other values (21)3340
20.1%

Most occurring characters

ValueCountFrequency (%)
S10081
22.0%
P8822
19.2%
33106
 
6.8%
i2936
 
6.4%
D2733
 
6.0%
X2303
 
5.0%
22294
 
5.0%
B1744
 
3.8%
61719
 
3.7%
C1575
 
3.4%
Other values (15)8609
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)45922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S10081
22.0%
P8822
19.2%
33106
 
6.8%
i2936
 
6.4%
D2733
 
6.0%
X2303
 
5.0%
22294
 
5.0%
B1744
 
3.8%
61719
 
3.7%
C1575
 
3.4%
Other values (15)8609
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S10081
22.0%
P8822
19.2%
33106
 
6.8%
i2936
 
6.4%
D2733
 
6.0%
X2303
 
5.0%
22294
 
5.0%
B1744
 
3.8%
61719
 
3.7%
C1575
 
3.4%
Other values (15)8609
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S10081
22.0%
P8822
19.2%
33106
 
6.8%
i2936
 
6.4%
D2733
 
6.0%
X2303
 
5.0%
22294
 
5.0%
B1744
 
3.8%
61719
 
3.7%
C1575
 
3.4%
Other values (15)8609
18.7%

Year
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)0.2%
Missing271
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2006.4064
Minimum1980
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:41.319662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1996
Q12003
median2007
Q32010
95-th percentile2015
Maximum2020
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.8289811
Coefficient of variation (CV)0.0029051846
Kurtosis1.8481806
Mean2006.4064
Median Absolute Deviation (MAD)4
Skewness-1.0025605
Sum32758598
Variance33.977021
MonotonicityNot monotonic
2026-01-19T20:32:41.542304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
20091431
 
8.6%
20081428
 
8.6%
20101259
 
7.6%
20071202
 
7.2%
20111139
 
6.9%
20061008
 
6.1%
2005941
 
5.7%
2002829
 
5.0%
2003775
 
4.7%
2004763
 
4.6%
Other values (29)5552
33.4%
ValueCountFrequency (%)
19809
 
0.1%
198146
0.3%
198236
0.2%
198317
 
0.1%
198414
 
0.1%
198514
 
0.1%
198621
0.1%
198716
 
0.1%
198815
 
0.1%
198917
 
0.1%
ValueCountFrequency (%)
20201
 
< 0.1%
20173
 
< 0.1%
2016344
 
2.1%
2015614
3.7%
2014582
3.5%
2013546
 
3.3%
2012657
4.0%
20111139
6.9%
20101259
7.6%
20091431
8.6%

Genre
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Action
3316 
Sports
2346 
Misc
1739 
Role-Playing
1488 
Shooter
1310 
Other values (7)
6399 

Length

Max length12
Median length10
Mean length7.1396554
Min length4

Characters and Unicode

Total characters118504
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSports
2nd rowPlatform
3rd rowRacing
4th rowSports
5th rowRole-Playing

Common Values

ValueCountFrequency (%)
Action3316
20.0%
Sports2346
14.1%
Misc1739
10.5%
Role-Playing1488
9.0%
Shooter1310
 
7.9%
Adventure1286
 
7.7%
Racing1249
 
7.5%
Platform886
 
5.3%
Simulation867
 
5.2%
Fighting848
 
5.1%
Other values (2)1263
 
7.6%

Length

2026-01-19T20:32:41.772470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action3316
20.0%
sports2346
14.1%
misc1739
10.5%
role-playing1488
9.0%
shooter1310
 
7.9%
adventure1286
 
7.7%
racing1249
 
7.5%
platform886
 
5.3%
simulation867
 
5.2%
fighting848
 
5.1%
Other values (2)1263
 
7.6%

Most occurring characters

ValueCountFrequency (%)
t12221
 
10.3%
o11523
 
9.7%
i11222
 
9.5%
n9054
 
7.6%
e6633
 
5.6%
r6509
 
5.5%
c6304
 
5.3%
l5311
 
4.5%
S5204
 
4.4%
a5171
 
4.4%
Other values (17)39352
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)118504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t12221
 
10.3%
o11523
 
9.7%
i11222
 
9.5%
n9054
 
7.6%
e6633
 
5.6%
r6509
 
5.5%
c6304
 
5.3%
l5311
 
4.5%
S5204
 
4.4%
a5171
 
4.4%
Other values (17)39352
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)118504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t12221
 
10.3%
o11523
 
9.7%
i11222
 
9.5%
n9054
 
7.6%
e6633
 
5.6%
r6509
 
5.5%
c6304
 
5.3%
l5311
 
4.5%
S5204
 
4.4%
a5171
 
4.4%
Other values (17)39352
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)118504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t12221
 
10.3%
o11523
 
9.7%
i11222
 
9.5%
n9054
 
7.6%
e6633
 
5.6%
r6509
 
5.5%
c6304
 
5.3%
l5311
 
4.5%
S5204
 
4.4%
a5171
 
4.4%
Other values (17)39352
33.2%
Distinct578
Distinct (%)3.5%
Missing58
Missing (%)0.3%
Memory size129.8 KiB
2026-01-19T20:32:42.390082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length28
Mean length13.634099
Min length3

Characters and Unicode

Total characters225508
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)1.2%

Sample

1st rowNintendo
2nd rowNintendo
3rd rowNintendo
4th rowNintendo
5th rowNintendo
ValueCountFrequency (%)
entertainment2479
 
8.2%
games1975
 
6.5%
interactive1621
 
5.4%
arts1354
 
4.5%
electronic1353
 
4.5%
activision1005
 
3.3%
digital940
 
3.1%
ubisoft935
 
3.1%
bandai932
 
3.1%
namco932
 
3.1%
Other values (644)16653
55.2%
2026-01-19T20:32:43.231203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t21384
 
9.5%
e19884
 
8.8%
i19282
 
8.6%
n18811
 
8.3%
a16979
 
7.5%
o13868
 
6.1%
13639
 
6.0%
r12034
 
5.3%
s9193
 
4.1%
m9026
 
4.0%
Other values (61)71408
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)225508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t21384
 
9.5%
e19884
 
8.8%
i19282
 
8.6%
n18811
 
8.3%
a16979
 
7.5%
o13868
 
6.1%
13639
 
6.0%
r12034
 
5.3%
s9193
 
4.1%
m9026
 
4.0%
Other values (61)71408
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)225508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t21384
 
9.5%
e19884
 
8.8%
i19282
 
8.6%
n18811
 
8.3%
a16979
 
7.5%
o13868
 
6.1%
13639
 
6.0%
r12034
 
5.3%
s9193
 
4.1%
m9026
 
4.0%
Other values (61)71408
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)225508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t21384
 
9.5%
e19884
 
8.8%
i19282
 
8.6%
n18811
 
8.3%
a16979
 
7.5%
o13868
 
6.1%
13639
 
6.0%
r12034
 
5.3%
s9193
 
4.1%
m9026
 
4.0%
Other values (61)71408
31.7%

NA_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct409
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26466743
Minimum0
Maximum41.49
Zeros4499
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:43.416969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08
Q30.24
95-th percentile1.06
Maximum41.49
Range41.49
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.81668303
Coefficient of variation (CV)3.0856952
Kurtosis649.13027
Mean0.26466743
Median Absolute Deviation (MAD)0.08
Skewness18.799627
Sum4392.95
Variance0.66697117
MonotonicityNot monotonic
2026-01-19T20:32:43.631675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04499
27.1%
0.02550
 
3.3%
0.01541
 
3.3%
0.03533
 
3.2%
0.05530
 
3.2%
0.04525
 
3.2%
0.06495
 
3.0%
0.07477
 
2.9%
0.08459
 
2.8%
0.09419
 
2.5%
Other values (399)7570
45.6%
ValueCountFrequency (%)
04499
27.1%
0.01541
 
3.3%
0.02550
 
3.3%
0.03533
 
3.2%
0.04525
 
3.2%
0.05530
 
3.2%
0.06495
 
3.0%
0.07477
 
2.9%
0.08459
 
2.8%
0.09419
 
2.5%
ValueCountFrequency (%)
41.491
< 0.1%
29.081
< 0.1%
26.931
< 0.1%
23.21
< 0.1%
15.851
< 0.1%
15.751
< 0.1%
14.971
< 0.1%
14.591
< 0.1%
14.031
< 0.1%
12.781
< 0.1%

EU_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct305
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14665201
Minimum0
Maximum29.02
Zeros5730
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:43.853074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.11
95-th percentile0.63
Maximum29.02
Range29.02
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.50535123
Coefficient of variation (CV)3.445921
Kurtosis756.0278
Mean0.14665201
Median Absolute Deviation (MAD)0.02
Skewness18.875535
Sum2434.13
Variance0.25537987
MonotonicityNot monotonic
2026-01-19T20:32:44.224374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05730
34.5%
0.011496
 
9.0%
0.021269
 
7.6%
0.03934
 
5.6%
0.04748
 
4.5%
0.05546
 
3.3%
0.06412
 
2.5%
0.07384
 
2.3%
0.08302
 
1.8%
0.09256
 
1.5%
Other values (295)4521
27.2%
ValueCountFrequency (%)
05730
34.5%
0.011496
 
9.0%
0.021269
 
7.6%
0.03934
 
5.6%
0.04748
 
4.5%
0.05546
 
3.3%
0.06412
 
2.5%
0.07384
 
2.3%
0.08302
 
1.8%
0.09256
 
1.5%
ValueCountFrequency (%)
29.021
< 0.1%
12.881
< 0.1%
11.011
< 0.1%
111
< 0.1%
9.271
< 0.1%
9.261
< 0.1%
9.231
< 0.1%
9.21
< 0.1%
8.891
< 0.1%
8.591
< 0.1%

JP_Sales
Real number (ℝ)

Zeros 

Distinct244
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07778166
Minimum0
Maximum10.22
Zeros10455
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:44.500971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.36
Maximum10.22
Range10.22
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.30929065
Coefficient of variation (CV)3.9763955
Kurtosis194.23399
Mean0.07778166
Median Absolute Deviation (MAD)0
Skewness11.206458
Sum1291.02
Variance0.095660705
MonotonicityNot monotonic
2026-01-19T20:32:44.731003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010455
63.0%
0.02728
 
4.4%
0.01704
 
4.2%
0.03528
 
3.2%
0.04397
 
2.4%
0.05321
 
1.9%
0.06290
 
1.7%
0.07231
 
1.4%
0.08211
 
1.3%
0.09156
 
0.9%
Other values (234)2577
 
15.5%
ValueCountFrequency (%)
010455
63.0%
0.01704
 
4.2%
0.02728
 
4.4%
0.03528
 
3.2%
0.04397
 
2.4%
0.05321
 
1.9%
0.06290
 
1.7%
0.07231
 
1.4%
0.08211
 
1.3%
0.09156
 
0.9%
ValueCountFrequency (%)
10.221
< 0.1%
7.21
< 0.1%
6.811
< 0.1%
6.51
< 0.1%
6.041
< 0.1%
5.651
< 0.1%
5.381
< 0.1%
5.331
< 0.1%
5.321
< 0.1%
4.871
< 0.1%

Other_Sales
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct157
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04806302
Minimum0
Maximum10.57
Zeros6477
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:44.958007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.04
95-th percentile0.2
Maximum10.57
Range10.57
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.1885884
Coefficient of variation (CV)3.9237735
Kurtosis1025.3481
Mean0.04806302
Median Absolute Deviation (MAD)0.01
Skewness24.233923
Sum797.75
Variance0.035565586
MonotonicityNot monotonic
2026-01-19T20:32:45.174035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06477
39.0%
0.013445
20.8%
0.021582
 
9.5%
0.03939
 
5.7%
0.04666
 
4.0%
0.05488
 
2.9%
0.06401
 
2.4%
0.07336
 
2.0%
0.08239
 
1.4%
0.09193
 
1.2%
Other values (147)1832
 
11.0%
ValueCountFrequency (%)
06477
39.0%
0.013445
20.8%
0.021582
 
9.5%
0.03939
 
5.7%
0.04666
 
4.0%
0.05488
 
2.9%
0.06401
 
2.4%
0.07336
 
2.0%
0.08239
 
1.4%
0.09193
 
1.2%
ValueCountFrequency (%)
10.571
< 0.1%
8.461
< 0.1%
7.531
< 0.1%
4.141
< 0.1%
3.311
< 0.1%
2.961
< 0.1%
2.931
< 0.1%
2.91
< 0.1%
2.851
< 0.1%
2.751
< 0.1%

Global_Sales
Real number (ℝ)

High correlation 

Distinct623
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53744066
Minimum0.01
Maximum82.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.8 KiB
2026-01-19T20:32:45.384427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.06
median0.17
Q30.47
95-th percentile2.04
Maximum82.74
Range82.73
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation1.5550279
Coefficient of variation (CV)2.8933947
Kurtosis603.93235
Mean0.53744066
Median Absolute Deviation (MAD)0.14
Skewness17.400645
Sum8920.44
Variance2.4181119
MonotonicityDecreasing
2026-01-19T20:32:45.598936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021071
 
6.5%
0.03811
 
4.9%
0.04645
 
3.9%
0.05632
 
3.8%
0.01618
 
3.7%
0.06577
 
3.5%
0.07510
 
3.1%
0.08483
 
2.9%
0.09434
 
2.6%
0.11421
 
2.5%
Other values (613)10396
62.6%
ValueCountFrequency (%)
0.01618
3.7%
0.021071
6.5%
0.03811
4.9%
0.04645
3.9%
0.05632
3.8%
0.06577
3.5%
0.07510
3.1%
0.08483
2.9%
0.09434
2.6%
0.1400
 
2.4%
ValueCountFrequency (%)
82.741
< 0.1%
40.241
< 0.1%
35.821
< 0.1%
331
< 0.1%
31.371
< 0.1%
30.261
< 0.1%
30.011
< 0.1%
29.021
< 0.1%
28.621
< 0.1%
28.311
< 0.1%

Interactions

2026-01-19T20:32:36.137886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:28.184269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:29.532882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:31.109622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:32.328091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:33.542902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:34.959100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:36.298506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:28.400630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:29.716088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:31.285065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:32.506562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:33.706527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:35.123958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:36.472764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:28.585429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:29.909206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:31.456217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:32.682088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:34.164925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:35.300610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:36.624018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:28.810007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:30.261037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:31.601444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:32.842265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:34.330223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:35.499568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:36.781770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:29.018311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:30.447888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:31.866840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:33.002435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:34.486080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:35.662884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:36.946355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:29.202458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:30.676807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:32.023280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:33.194309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:34.640479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:35.828604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:37.098633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:29.370120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:30.888324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:32.179047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:33.375260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:34.808432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-19T20:32:35.982796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-19T20:32:45.771189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EU_SalesGenreGlobal_SalesJP_SalesNA_SalesOther_SalesPlatformRankYear
EU_Sales1.0000.0120.697-0.1770.6810.7660.022-0.697-0.058
Genre0.0121.0000.0230.0440.0290.0080.1750.0880.109
Global_Sales0.6970.0231.0000.1520.7960.8100.051-1.000-0.151
JP_Sales-0.1770.0440.1521.000-0.229-0.0700.125-0.1520.010
NA_Sales0.6810.0290.796-0.2291.0000.7690.068-0.796-0.133
Other_Sales0.7660.0080.810-0.0700.7691.0000.000-0.8100.056
Platform0.0220.1750.0510.1250.0680.0001.0000.1360.633
Rank-0.6970.088-1.000-0.152-0.796-0.8100.1361.0000.152
Year-0.0580.109-0.1510.010-0.1330.0560.6330.1521.000

Missing values

2026-01-19T20:32:37.336940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-19T20:32:37.547372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-19T20:32:37.858548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RankNamePlatformYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_Sales
01Wii SportsWii2006.0SportsNintendo41.4929.023.778.4682.74
12Super Mario Bros.NES1985.0PlatformNintendo29.083.586.810.7740.24
23Mario Kart WiiWii2008.0RacingNintendo15.8512.883.793.3135.82
34Wii Sports ResortWii2009.0SportsNintendo15.7511.013.282.9633.00
45Pokemon Red/Pokemon BlueGB1996.0Role-PlayingNintendo11.278.8910.221.0031.37
56TetrisGB1989.0PuzzleNintendo23.202.264.220.5830.26
67New Super Mario Bros.DS2006.0PlatformNintendo11.389.236.502.9030.01
78Wii PlayWii2006.0MiscNintendo14.039.202.932.8529.02
89New Super Mario Bros. WiiWii2009.0PlatformNintendo14.597.064.702.2628.62
910Duck HuntNES1984.0ShooterNintendo26.930.630.280.4728.31
RankNamePlatformYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_Sales
1658816591Mega Brain BoostDS2008.0PuzzleMajesco Entertainment0.010.000.000.00.01
1658916592Chou Ezaru wa Akai Hana: Koi wa Tsuki ni Shirube KareruPSV2016.0Actiondramatic create0.000.000.010.00.01
1659016593Eiyuu Densetsu: Sora no Kiseki Material Collection PortablePSP2007.0Role-PlayingFalcom Corporation0.000.000.010.00.01
1659116594Myst IV: RevelationPC2004.0AdventureUbisoft0.010.000.000.00.01
1659216595PlusheesDS2008.0SimulationDestineer0.010.000.000.00.01
1659316596Woody Woodpecker in Crazy Castle 5GBA2002.0PlatformKemco0.010.000.000.00.01
1659416597Men in Black II: Alien EscapeGC2003.0ShooterInfogrames0.010.000.000.00.01
1659516598SCORE International Baja 1000: The Official GamePS22008.0RacingActivision0.000.000.000.00.01
1659616599Know How 2DS2010.0Puzzle7G//AMES0.000.010.000.00.01
1659716600Spirits & SpellsGBA2003.0PlatformWanadoo0.010.000.000.00.01